{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mpl.rcParams[u'font.sans-serif'] = ['simhei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['xgb3.csv', 'xgb6.csv', '.ipynb_checkpoints', 'xgb4.csv', 'xgb0.csv', 'xgb2.csv', 'xgb5.csv', 'xgb1.csv', 'xgb7.csv']\n"
     ]
    }
   ],
   "source": [
    "files = os.listdir('./preds')\n",
    "print(files)\n",
    "pred = pd.read_csv('./preds/'+files[0])\n",
    "score = pred.score\n",
    "for f in files[1:]:\n",
    "    if f == '.ipynb_checkpoints':\n",
    "        continue\n",
    "    pred = pd.read_csv('./preds/'+f)\n",
    "    score += pred.score\n",
    "\n",
    "score /= len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'tag的预测概率值')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(score)\n",
    "plt.title('valid集的tag预测概率值分布')\n",
    "plt.ylabel(s = '数量')\n",
    "plt.xlabel(s = 'tag的预测概率值')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
